As robotic navigation techniques in perception and planning advance, mobile robots increasingly venture into off-road environments involving complex traversability. However, selecting suitable planning methods remains a challenge due to their algorithmic diversity, as each offers unique benefits. To aid in algorithm design, we introduce BenchNav, an open-source PyTorch-based simulation platform for benchmarking off-road navigation with uncertain traversability. Built upon Gymnasium, BenchNav provides three key features: 1) a data generation pipeline for preparing synthetic natural environments, 2) built-in machine learning models for traversability prediction, and 3) consistent execution of path and motion planning across different algorithms. We show BenchNav's versatility through simulation examples in off-road environments, employing three representative planning algorithms from different domains.

@article{endo2024benchnav,

  title={BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability},

  author={Endo, Masafumi and Honda, Kohei and Ishigami, Genya},

  journal={arXiv preprint arXiv:2405.13318},

  year={2024}}